--- name: cargo-analytics description: "Get data out of Cargo and measure what ran — download a run output, export a segment or model to CSV or JSON, and pull run and batch success and error counts. Triggers: \"download the results\", \"export this to CSV\", \"give me the file\", \"how many succeeded\", \"what is my error rate\", \"send me the enriched list\", \"get the output of that run\", \"how many records did it write\". Skip when: asking why something failed or where credits went — use cargo-diagnostics; asking about credits, plans, or invoices — use cargo-billing." version: "1.4.3" compatibility: Requires @cargo-ai/cli (npm). Sign in or create an account with `cargo-ai login --email` (emailed code, no browser), `--oauth`, or an API token homepage: https://github.com/getcargohq/cargo-skills metadata: author: getcargo openclaw: requires: bins: - cargo-ai install: - kind: node package: "@cargo-ai/cli@latest" bins: - cargo-ai homepage: https://github.com/getcargohq/cargo-skills --- # Cargo CLI — Analytics Measurement and export: monitoring run metrics, downloading run and batch results, and exporting segment data. > See `references/response-shapes.md` for full JSON response structures. > See `references/troubleshooting.md` for common errors and how to fix them. > See `references/examples/run-analytics.md` for run metrics and error monitoring. > See `references/examples/exports.md` for data export and download examples. > For billing, usage metrics, and subscription: use the `cargo-billing` skill. ## Bootstrap Already signed in (`cargo-ai whoami` returns a workspace)? Skip to the next section. ```bash npm install -g @cargo-ai/cli # no global install? prefix every command with `npx @cargo-ai/cli` cargo-ai login --email you@company.com # emailed code, no browser; creates the account on first use # alternatives: --oauth (browser) · --token (CI) cargo-ai whoami # confirm the active workspace before any write ``` Every command prints JSON to stdout; failures exit non-zero with `{"errorMessage": "..."}`. Anything that creates a run or a batch is async — pass `--wait-until-finished` or poll the matching `get`. When the full skill bundle is installed, [`../cargo/references/prerequisites.md`](../cargo/references/prerequisites.md) adds the CLI version pin, token scopes, and the admin-only surface. ## Scope — measure and export, not explain This skill answers **"what happened"** and **"give me the data"**: metrics, counts, downloads, exports. The moment the question becomes **"why"** — why did this run fail, why is the output wrong or empty, which root cause explains these errors, why is this play so expensive — switch to the `cargo-diagnostics` skill; its runbooks sequence the raw surfaces into a diagnosis. | The question sounds like… | Load | | --- | --- | | "What's the error rate?" / "How many runs failed this week?" / "Export the results / segment" | **this skill** | | "Why did this run fail?" / "Run succeeded but the output looks wrong" | `cargo-diagnostics` → `references/run-trace.md` | | "Why does this batch have errors? Which node keeps failing, and is it one cause or many?" | `cargo-diagnostics` → `references/batch-error-sweep.md` | | "Why is this play so expensive? Where do the credits go?" | `cargo-diagnostics` → `references/play-optimize-credits.md` | The two skills chain naturally: analytics **detects** (error rate spiked, batch reports failures), diagnostics **explains** (18 of 20 failures share one root cause), then analytics **retrieves** the clean results once the cause is fixed and the runs re-executed. ## Discover resources first Most analytics commands require UUIDs. Discover them before querying. ```bash cargo-ai orchestration play list # all plays (name, workflowUuid) cargo-ai orchestration tool list # all tools (name, workflowUuid) cargo-ai orchestration workflow list # all workflows (uuid only — no name) cargo-ai ai agent list # all agents (uuid, name) cargo-ai connection connector list # all connectors (uuid, name, integrationSlug) cargo-ai storage model list # all models (uuid, name, slug) ``` ## Quick reference ```bash cargo-ai orchestration run get-metrics --workflow-uuid cargo-ai orchestration run download --workflow-uuid --is-finished cargo-ai orchestration run count --workflow-uuid --statuses error cargo-ai orchestration query execute "SELECT status, count() FROM runs GROUP BY status" cargo-ai segmentation segment download --model-uuid --filter '{"conjonction":"and","groups":[]}' ``` **Picking the right command:** - `run get-metrics` / `run count` — workflow-scoped, predefined aggregations. Best when you already have a `workflowUuid`. - `orchestration query execute` — ad-hoc SQL across the entire workspace (`runs`, `batches`, `spans`, `records`). Best for cross-workflow analytics, per-node breakdowns, and time-series. - `run download` / `run download-outputs` — per-record output retrieval. - `segment download` / `storage query execute` — storage data (Companies, Contacts, …). ## Workflow run metrics Aggregated metrics for workflow runs (success/error rates, credits per node). ```bash # Metrics for a workflow cargo-ai orchestration run get-metrics --workflow-uuid # Scoped to a release, batch, or date range cargo-ai orchestration run get-metrics --workflow-uuid --release-uuid cargo-ai orchestration run get-metrics --workflow-uuid --batch-uuid cargo-ai orchestration run get-metrics --workflow-uuid \ --created-after --created-before ``` ## Run count Count runs matching specific criteria — useful for monitoring. ```bash cargo-ai orchestration run count --workflow-uuid --statuses error cargo-ai orchestration run count --workflow-uuid --is-finished \ --created-after --created-before cargo-ai orchestration run count --workflow-uuid --batch-uuid ``` Supports: `--statuses`, `--batch-uuid`, `--release-uuid`, `--is-finished`, `--created-after`, `--created-before`, `--record-id`, `--record-title`. For cross-workflow analytics or shapes that `run count` doesn't expose (per-node failure breakdowns, p95 durations, error rate over time), use `orchestration query execute` — see the [Ad-hoc execution analytics](#ad-hoc-execution-analytics-orchestration-query) section. ## Ad-hoc execution analytics (`orchestration query`) Run SQL against orchestration runtime tables — `runs`, `batches`, `spans`, `records` — for analytics that the canned metrics commands don't cover. Tables are referenced without a schema prefix; workspace scoping is automatic. See `cargo-orchestration/references/examples/queries.md` for schemas and limits. ```bash # Error rate across the workspace in the last day cargo-ai orchestration query execute \ "SELECT countIf(status='error') / count() AS error_rate FROM runs WHERE created_at > now() - INTERVAL 1 DAY" # Failed runs per workflow this week cargo-ai orchestration query execute \ "SELECT workflow_uuid, count() AS errors FROM runs WHERE status='error' AND created_at > now() - INTERVAL 7 DAY GROUP BY workflow_uuid ORDER BY errors DESC" # Per-node failure counts (last 24h) cargo-ai orchestration query execute \ "SELECT node_slug, count() AS failures FROM spans WHERE execution_status='error' AND execution_started_at > now() - INTERVAL 1 DAY GROUP BY node_slug ORDER BY failures DESC" # Credit spend by workflow this month cargo-ai orchestration query execute \ "SELECT workflow_uuid, sum(credits_used_count) AS credits FROM batches WHERE created_at >= toStartOfMonth(now()) GROUP BY workflow_uuid ORDER BY credits DESC" ``` Read-only and capped: 30s execution time, 10 000 result rows, 10 000 000 rows scanned. Narrow with a `created_at`/`execution_started_at` predicate to stay under the row-scan cap. ## Downloading run results Two distinct commands — pick the right one for the job. ### `run download` — full run records (metadata + per-node `runContext`) Returns each run as a JSON object with status, timing, executions, and `runContext.` containing per-node outputs. Best for debugging or when you need the full execution history. ```bash # All finished runs cargo-ai orchestration run download --workflow-uuid --is-finished # Date range cargo-ai orchestration run download --workflow-uuid \ --created-after --created-before # Specific statuses cargo-ai orchestration run download --workflow-uuid --statuses success,error # From a specific batch cargo-ai orchestration run download --workflow-uuid --batch-uuid ``` ### `run download-outputs` — output of a specific node (CSV/JSON via signed URL) **This is the canonical way to get action results out of the platform.** Maps to API `POST /v1/orchestration/runs/download-outputs`. Returns `{"url": "..."}` — a signed URL to a CSV (default) or JSON file containing only the output node's data with input/output context. Faster and cheaper than downloading whole run records when you only need the result. ```bash # Required: --workflow-uuid + --output-node-slug cargo-ai orchestration run download-outputs \ --workflow-uuid \ --output-node-slug \ --format json \ --is-finished # Filter by batch + status cargo-ai orchestration run download-outputs \ --workflow-uuid \ --output-node-slug \ --batch-uuid \ --statuses finished ``` To find the `output-node-slug`: `cargo-ai orchestration release get ` → look at `nodes[].slug`. The terminal output node is typically named `output` or `end`. ## Downloading batch results ```bash cargo-ai orchestration batch download --uuid --output-node-slug ``` To find the `output-node-slug`: run `cargo-ai orchestration release get ` (get the release UUID from the batch) and look at `nodes[].slug`. ## Handling partial batch failures A batch with `status: "success"` can still contain individual run failures. Always inspect the batch for errors before treating results as complete. **Step 1 — Check the batch summary:** ```bash cargo-ai orchestration batch get # → .runsCount = total records submitted # → .executedRunsCount = records that reached a terminal state (success or error) # → .failedRunsCount = records that errored ``` **Step 2 — Count and download the failed runs:** ```bash cargo-ai orchestration run count \ --workflow-uuid \ --batch-uuid \ --statuses error cargo-ai orchestration run download \ --workflow-uuid \ --batch-uuid \ --statuses error ``` **Step 3 — Diagnose.** Working out *why* they failed — grouping failures by root cause, picking exemplar runs, reading `runContext` — is the `cargo-diagnostics` skill's job: load `../cargo-diagnostics/references/batch-error-sweep.md` and feed it the batch UUID. **Step 4 — Re-run only the failed records:** After the diagnosis and fixing the underlying issue (connector credentials, bad input data, rate limits): ```bash # Extract record IDs from the failed run download, then: cargo-ai orchestration batch create \ --workflow-uuid \ --data '{"kind":"recordIds","recordIds":["id1","id2","id3"]}' ``` **Filtering by node output slug:** To download only a specific node's output from a batch (e.g. just the enrichment node, not the full run): ```bash # 1. Get the release UUID from the batch cargo-ai orchestration batch get # → .releaseUuid # 2. Find the node slug cargo-ai orchestration release get # → nodes[].slug # 3. Download that node's output cargo-ai orchestration batch download \ --uuid \ --output-node-slug ``` ## Segment data export Filter JSON uses `conjonction` (not `conjunction`) — this is intentional. See the `cargo-orchestration` skill's `references/filter-syntax.md` for the full filter syntax. ```bash # Full export (all records) cargo-ai segmentation segment download \ --model-uuid \ --filter '{"conjonction":"and","groups":[]}' # With sorting and limit cargo-ai segmentation segment download \ --model-uuid \ --filter '{"conjonction":"and","groups":[]}' \ --sort '[{"columnSlug":"created_at","kind":"desc"}]' \ --limit 1000 ``` **IMPORTANT:** `segment download` requires `--model-uuid`, not `--segment-uuid`. Get the `modelUuid` from `segment list`. For live paginated queries with enrichment, use `segmentation segment fetch` from the `cargo-orchestration` skill. ## Help Every command supports `--help`: ```bash cargo-ai billing usage get-metrics --help cargo-ai orchestration run download --help cargo-ai segmentation segment download --help ```